AI Integration Timeline: What Really Takes Weeks

A mid-size distributor in Gebze asked us in late spring to add a chatbot to their customer service line and automate order status replies. The request sounded like a three-week job. Six weeks in, the bot still could not pull accurate stock data, because the inventory system it needed to query had no usable API, and three different departments kept conflicting spreadsheets as backup.

We see this pattern often enough that it deserves to be named directly. In most AI integration projects, the timeline problem rarely comes from the AI model itself.

The visible symptom is always the same: wrong or incomplete answers

In the Gebze case, the chatbot answered shipping policy questions correctly within days. It struggled with anything tied to live data: current stock levels, order status, delivery estimates. Customers got generic replies or, worse, confident-sounding wrong answers, which does more damage than no automation at all.

The client's first instinct was to blame the language model. They asked us to try a different provider, assuming a smarter model would somehow infer correct stock numbers it was never given. A model can only be as accurate as the data pipeline that feeds it, and no amount of model swapping fixes a missing data source.

The root cause is usually data ownership, not integration

When we traced the stock data back to its source, we found it living in an on-premise ERP system with no documented API, accessed only through a desktop client that three warehouse staff used manually. There was no single source of truth. Finance kept one export, logistics kept another, and neither updated in real time.

This is the infrastructure gap that delays almost every AI integration project we take on. The client had budgeted for the AI layer and assumed the data layer already existed in usable form. It did not, and building a reliable data access point took longer than building the chatbot logic itself.

What the client tried first, and why it fell short

Before calling us, the internal team tried connecting the chatbot platform directly to a nightly CSV export from the ERP. It worked for about a day. Stock numbers were twelve hours stale by the time a customer asked, and the export occasionally failed silently when warehouse staff logged out mid-sync.

They also tried hardcoding frequently asked product categories into the bot's responses, hoping to buy time while a proper connection got built. That patched the obvious complaints but created a maintenance burden: every price change or stock-out meant manually editing bot scripts, which defeated the purpose of automating the task in the first place.

How long the actual fix took, and what it involved

A realistic AI integration project for a case like this runs three to eight weeks, and the data layer work usually eats more than half of that time. For the Gebze client specifically, we spent the first two weeks mapping their ERP's internal database structure and building a lightweight API layer on top of it, since no vendor-supported API existed.

Once that API existed, the chatbot integration itself took under two weeks: connecting the conversational flow to real endpoints, handling edge cases like out-of-stock responses, and testing against actual customer queries pulled from their support logs. The remaining time went to testing with real order data and training their support staff to monitor and override the bot when needed.

If your systems already expose clean APIs and consistent data, a comparable chatbot or automation project can land on the shorter end of that three-to-eight-week range. If your data sits in spreadsheets, legacy desktop software, or multiple unsynced systems, plan for the longer end, and budget separately for the data access work before the AI logic even starts. We cover this kind of integration work in more detail on our AI solutions page, including how we scope the data layer before committing to a delivery date.

What infrastructure needs to be ready before you start

Before any AI integration kickoff, we ask clients to check a short list of things, because gaps here are what push projects from six weeks to sixteen.

• A documented way to access the data the AI needs, whether that is a REST API, a database with read access, or at minimum a reliably scheduled export with a defined format.

• One agreed source of truth for each data type involved. If stock levels live in three systems, decide which one the bot trusts before building anything.

• Basic data hygiene, meaning consistent product naming, no duplicate records with conflicting values, and a clear sense of how often the data actually changes.

• Staff availability for testing, since someone on the client side needs to spend real time reviewing bot responses against real scenarios rather than just approving a demo.

• A clear answer to what happens when the AI is wrong or unsure, including who gets notified and how a human takes over the conversation.

None of these require expensive tooling. Most of this is organizational clarity rather than software spend, and sorting it out before a project starts is the single biggest lever on whether the timeline holds.

What a faster but narrower scope looks like

Not every AI integration needs to touch live ERP data. If your use case is answering policy questions, summarizing documents, or handling a narrow set of predictable requests, the three-week version of this project is realistic, because there is no fragile data pipeline to build.

The trade-off is scope. A bot that only answers from a static knowledge base cannot tell a customer their order is delayed. If that limitation is acceptable for your business right now, you get a working tool in a fraction of the time, and you can connect live data sources later as a second phase instead of trying to solve everything at once. We have scoped projects this way for clients who wanted something live quickly and were comfortable expanding it incrementally; you can see examples of similar staged work in our portfolio. For the Gebze client, we eventually proposed exactly this kind of phased approach for their next automation request: an internal tool for the sales team. Phase one used already-clean CRM data and took under four weeks. Phase two, which will eventually pull in logistics data, is scheduled separately once that team finishes cleaning up their own records.

If you are scoping a similar project and want a realistic sense of where your own data stands before committing to a timeline, our team is happy to walk through it with you on a quote request.